"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	int ):
     lowercase_      :							Dict						=      [
         'encoder.version',
         'decoder.version',
         'model.encoder.version',
         'model.decoder.version',
         '_float_tensor',
         'decoder.output_projection.weight',
     ]
     for k in ignore_keys:
          state_dict.pop(a__	,     a__ )



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	str ):
     lowercase_      :							str						=      emb.weight.shape
     lowercase_      :							Optional[Any]						=      nn.Linear(a__	,     a__	,     bias=a__ )
     lowercase_      :							str						=      emb.weight.data
     return lin_layer



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Dict	,     __SCREAMING_SNAKE_CASE				:	Dict="facebook/mbart-large-en-ro"	,     __SCREAMING_SNAKE_CASE				:	Tuple=False	,     __SCREAMING_SNAKE_CASE				:	Optional[int]=False ):
     lowercase_      :							int						=      torch.load(a__	,     map_location='cpu' )['model']
     remove_ignore_keys_(a__ )
     lowercase_      :							Tuple						=      state_dict['encoder.embed_tokens.weight'].shape[0]

     lowercase_      :							List[Any]						=      MBartConfig.from_pretrained(a__	,     vocab_size=a__ )
     if mbart_aa and finetuned:
          lowercase_      :							Any						=      'relu'

     lowercase_      :							List[Any]						=      state_dict['decoder.embed_tokens.weight']
     lowercase_      :							str						=      MBartForConditionalGeneration(a__ )
     model.model.load_state_dict(a__ )

     if finetuned:
          lowercase_      :							str						=      make_linear_from_emb(model.model.shared )

     return model


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE             =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
       )
       parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument(
           "--hf_config",
           default="facebook/mbart-large-cc25",
           type=str,
           help="Which huggingface architecture to use: mbart-large",
       )
       parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
       parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
       __SCREAMING_SNAKE_CASE             =parser.parse_args()
       __SCREAMING_SNAKE_CASE             =convert_fairseq_mbart_checkpoint_from_disk(
           args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
       )
       model.save_pretrained(args.pytorch_dump_folder_path)









"""simple docstring"""


import itertools
import math


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5     ,      int(math.sqrt(a__ ) + 1 )     ,      6 ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			2
						while True:
												if is_prime(a__ ):
																		yield num
												num += 1


def        UpperCAmelCase      (					a__ = 1_00_01 ):





						'''simple docstring'''






						return next(itertools.islice(prime_generator()     ,      nth - 1     ,      a__ ) )


if __name__ == "__main__":
		print(F"""{solution() = }""")